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    "environment": {
      "name": "tf2-gpu.2-3.m79",
      "type": "gcloud",
      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m79"
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      "version": "3.7.10"
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    "colab": {
      "name": "bigquery_tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "WfzRLGlzw0go"
      },
      "source": [
        "# How to Efficiently Read BigQuery Data from TensorFlow 2.3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLCPVgwDw9Oy"
      },
      "source": [
        "## Learning Objectives\n",
        "\n",
        "* Build a benchmark model.\n",
        "* Find the breakoff point for Keras.\n",
        "* Training a TensorFlow/Keras model that reads from BigQuery.\n",
        "* Load TensorFlow model into BigQuery.\n",
        "\n",
        "## Introduction\n",
        "\n",
        "In this notebook, you learn \n",
        "[\"How to efficiently read BigQuery data from TensorFlow 2.x\"](https://medium.com/@lakshmanok/how-to-read-bigquery-data-from-tensorflow-2-0-efficiently-9234b69165c8)\n",
        "\n",
        "\n",
        "The example problem is to find credit card fraud from the dataset published in:\n",
        "<i>\n",
        "Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
        "</i>\n",
        "and available in BigQuery at <pre>bigquery-public-data.ml_datasets.ulb_fraud_detection</pre>\n",
        "\n",
        "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/bigquery_tensorflow.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEQtdAuiw0gp"
      },
      "source": [
        "## Build a benchmark model\n",
        "\n",
        "In order to compare things, we will do a simple logistic regression in BigQuery ML.\n",
        "\n",
        "Note that we are using all the columns in the dataset as predictors (except for the Time and Class columns).\n",
        "The Time column is used to split the dataset 80:20 with the first 80% used for training and the last 20% used for evaluation.\n",
        "We will also have BigQuery ML automatically balance the weights.\n",
        "\n",
        "Because the Amount column has a huge range, we take the log of it in preprocessing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afjJfioww0gq",
        "outputId": "53258b8e-437e-4ab6-dc0f-262c0c8fc86b"
      },
      "source": [
        "%%bash\n",
        "# create output dataset\n",
        "bq mk advdata"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset 'qwiklabs-gcp-03-5b2f0816822f:advdata' successfully created.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rk1Vzp7ww0gr",
        "outputId": "7d5ef311-0c70-4f49-d089-bf260a5069f2"
      },
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE MODEL advdata.ulb_fraud_detection \n",
        "TRANSFORM(\n",
        "    * EXCEPT(Amount),\n",
        "    SAFE.LOG(Amount) AS log_amount\n",
        ")\n",
        "OPTIONS(\n",
        "    INPUT_LABEL_COLS=['class'],\n",
        "    AUTO_CLASS_WEIGHTS = TRUE,\n",
        "    DATA_SPLIT_METHOD='seq',\n",
        "    DATA_SPLIT_COL='Time',\n",
        "    MODEL_TYPE='logistic_reg'\n",
        ") AS\n",
        "\n",
        "SELECT \n",
        " *\n",
        "FROM `bigquery-public-data.ml_datasets.ulb_fraud_detection`"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.01s: 100%|██████████| 3/3 [00:00<00:00, 1845.81query/s]                        \n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "Empty DataFrame\n",
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        "id": "jxsgtaPdw0gs",
        "outputId": "63604fc5-71e2-472f-e959-a2b315ea0606"
      },
      "source": [
        "%%bigquery\n",
        "# Use the ML.EVALUATE function to evaluate model metrics\n",
        "SELECT * FROM TODO: ___________(MODEL advdata.ulb_fraud_detection)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 589.09query/s]                          \n",
            "Downloading: 100%|██████████| 1/1 [00:01<00:00,  1.38s/rows]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>roc_auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.051898</td>\n",
              "      <td>0.893333</td>\n",
              "      <td>0.978371</td>\n",
              "      <td>0.098097</td>\n",
              "      <td>0.119662</td>\n",
              "      <td>0.985345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   precision    recall  accuracy  f1_score  log_loss   roc_auc\n",
              "0   0.051898  0.893333  0.978371  0.098097  0.119662  0.985345"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "6u5XPNLbw0gs",
        "outputId": "1555eec7-0ac0-4433-fa5c-0746af8a8eeb"
      },
      "source": [
        "%%bigquery\n",
        "SELECT predicted_class_probs, Class\n",
        "# The ML.PREDICT function is used to predict outcomes using the model\n",
        "FROM TODO: ___________( MODEL advdata.ulb_fraud_detection,\n",
        "  (SELECT * FROM `bigquery-public-data.ml_datasets.ulb_fraud_detection` WHERE Time = 85285.0)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.00s: 100%|██████████| 2/2 [00:00<00:00, 1095.83query/s]                        \n",
            "Downloading: 100%|██████████| 5/5 [00:01<00:00,  3.29rows/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predicted_class_probs</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[{'label': 1, 'prob': 0.08118664806042943}, {'...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[{'label': 1, 'prob': 0.999999999367521}, {'la...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[{'label': 1, 'prob': 0.999999999367521}, {'la...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[{'label': 1, 'prob': 0.9999999994844684}, {'l...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[{'label': 1, 'prob': 0.9999999994844684}, {'l...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               predicted_class_probs  Class\n",
              "0  [{'label': 1, 'prob': 0.08118664806042943}, {'...      0\n",
              "1  [{'label': 1, 'prob': 0.999999999367521}, {'la...      1\n",
              "2  [{'label': 1, 'prob': 0.999999999367521}, {'la...      1\n",
              "3  [{'label': 1, 'prob': 0.9999999994844684}, {'l...      1\n",
              "4  [{'label': 1, 'prob': 0.9999999994844684}, {'l...      1"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27Lx3pIzw0gt"
      },
      "source": [
        "## Find the breakoff point for Keras\n",
        "\n",
        "When we do the training in Keras & TensorFlow, we need to find the place to split the dataset and how to weight the imbalanced data.\n",
        "(BigQuery ML did that for us because we specified 'seq' as the split method and auto_class_weights to be True)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBSfIyClw0gt",
        "outputId": "7f3b6248-0155-4aca-f162-127e3184bba4"
      },
      "source": [
        "%%bigquery\n",
        "WITH counts AS (\n",
        "SELECT\n",
        "    APPROX_QUANTILES(Time, 5)[OFFSET(4)] AS train_cutoff\n",
        "    , COUNTIF(CLASS > 0) AS pos\n",
        "    , COUNTIF(CLASS = 0) AS neg\n",
        "FROM `bigquery-public-data`.ml_datasets.ulb_fraud_detection\n",
        ")\n",
        "\n",
        "SELECT\n",
        "   train_cutoff\n",
        "    , SAFE.LOG(SAFE_DIVIDE(pos,neg)) AS output_bias\n",
        "    , 0.5*SAFE_DIVIDE(pos + neg, pos) AS weight_pos\n",
        "    , 0.5*SAFE_DIVIDE(pos + neg, neg) AS weight_neg\n",
        "FROM TODO: ___________ # Table Name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 708.62query/s] \n",
            "Downloading: 100%|██████████| 1/1 [00:01<00:00,  1.47s/rows]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_cutoff</th>\n",
              "      <th>output_bias</th>\n",
              "      <th>weight_pos</th>\n",
              "      <th>weight_neg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>144803.0</td>\n",
              "      <td>-6.359359</td>\n",
              "      <td>289.438008</td>\n",
              "      <td>0.500865</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_cutoff  output_bias  weight_pos  weight_neg\n",
              "0      144803.0    -6.359359  289.438008    0.500865"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Cfhdtftw0gu"
      },
      "source": [
        "The time cutoff is 144803 and the Keras model's output bias needs to be set at -6.36\n",
        "The class weights need to be 289.4 and 0.5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7BhcV53w0gu"
      },
      "source": [
        "## Training a TensorFlow/Keras model that reads from BigQuery\n",
        "\n",
        "Create the dataset from BigQuery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPI6LsXJw0gu"
      },
      "source": [
        "# import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.framework import dtypes\n",
        "from tensorflow_io.bigquery import BigQueryClient\n",
        "from tensorflow_io.bigquery import BigQueryReadSession\n",
        "\n",
        "def features_and_labels(features):\n",
        "  label = features.pop('Class') # this is what we will train for\n",
        "  return features, label\n",
        "\n",
        "def read_dataset(client, row_restriction, batch_size=2048):\n",
        "    GCP_PROJECT_ID='qwiklabs-gcp-03-5b2f0816822f'  # CHANGE\n",
        "    COL_NAMES = ['Time', 'Amount', 'Class'] + ['V{}'.format(i) for i in range(1,29)]\n",
        "    COL_TYPES = [dtypes.float64, dtypes.float64, dtypes.int64] + [dtypes.float64 for i in range(1,29)]\n",
        "    DATASET_GCP_PROJECT_ID, DATASET_ID, TABLE_ID,  = 'bigquery-public-data.ml_datasets.ulb_fraud_detection'.split('.')\n",
        "    bqsession = client.read_session(\n",
        "        \"projects/\" + GCP_PROJECT_ID,\n",
        "        DATASET_GCP_PROJECT_ID, TABLE_ID, DATASET_ID,\n",
        "        COL_NAMES, COL_TYPES,\n",
        "        requested_streams=2,\n",
        "        row_restriction=row_restriction)\n",
        "    dataset = bqsession.parallel_read_rows()\n",
        "    return dataset.prefetch(1).map(features_and_labels).shuffle(batch_size*10).batch(batch_size)\n",
        "\n",
        "client = BigQueryClient()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53GS5Cviw0gv",
        "outputId": "8c111e09-5c0c-4419-eb83-d5cd4532a01e"
      },
      "source": [
        "temp_df = TODO: ___________(client, 'Time <= 144803', 2) # Function Name\n",
        "for row in temp_df:\n",
        "    print(row)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(OrderedDict([('Amount', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0., 0.])>), ('Time', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([403., 430.])>), ('V1', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 1.2374128 , -1.86025769])>), ('V10', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.51128378, -0.3716215 ])>), ('V11', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.17815885,  0.85974116])>), ('V12', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.76290909, 0.37260884])>), ('V13', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 1.70092326, -1.24018477])>), ('V14', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.13386062,  0.99839133])>), ('V15', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.40241775, -0.34638713])>), ('V16', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.68466826, -0.39167858])>), ('V17', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.60939528,  0.34828943])>), ('V18', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.70427652,  0.28212477])>), ('V19', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.72072551,  1.16589317])>), ('V2', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.51236483, -0.62985892])>), ('V20', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.0067162 ,  0.32044963])>), ('V21', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.07754305,  0.2680278 ])>), ('V22', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.17821966,  0.12551524])>), ('V23', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.03872219, -0.22502854])>), ('V24', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.47121815, 0.58666444])>), ('V25', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.28924869, -0.03159808])>), ('V26', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.87180308, 0.57016802])>), ('V27', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.06688386, -0.04300749])>), ('V28', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.01298638, -0.22373948])>), ('V3', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.68774556, 0.96657045])>), ('V4', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([1.69387239, 0.84463208])>), ('V5', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.23632262,  0.75998266])>), ('V6', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.65023174, -1.4811729 ])>), ('V7', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([ 0.11806621, -0.50968145])>), ('V8', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.23054532,  0.54072208])>), ('V9', <tf.Tensor: shape=(2,), dtype=float64, numpy=array([-0.80852253, -0.73362339])>)]), <tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 0])>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4p5vhh6Aw0gv"
      },
      "source": [
        "train_df = read_dataset(client, 'Time <= 144803', 2048)\n",
        "eval_df = read_dataset(client, 'Time > 144803', 2048)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L03qs7Oew0gw"
      },
      "source": [
        "Create Keras model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRgUFvQDw0gw",
        "outputId": "385870a4-348b-4a6f-d5e6-2f412386cb64"
      },
      "source": [
        "metrics = [\n",
        "      tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "      tf.keras.metrics.Precision(name='precision'),\n",
        "      tf.keras.metrics.Recall(name='recall'),\n",
        "      tf.keras.metrics.AUC(name='roc_auc'),\n",
        "]\n",
        "\n",
        "# create inputs, and pass them into appropriate types of feature columns (here, everything is numeric)\n",
        "inputs = {\n",
        "    'V{}'.format(i) : tf.keras.layers.Input(name='V{}'.format(i), shape=(), dtype='float64') for i in range(1, 29)\n",
        "}\n",
        "inputs['Amount'] = tf.keras.layers.Input(name='Amount', shape=(), dtype='float64')\n",
        "input_fc = [tf.feature_column.numeric_column(colname) for colname in inputs.keys()]\n",
        "\n",
        "# transformations. only the Amount is transformed\n",
        "transformed = inputs.copy()\n",
        "transformed['Amount'] = tf.keras.layers.Lambda(\n",
        "    lambda x: tf.math.log(tf.math.maximum(x, 0.01)), name='log_amount')(inputs['Amount'])\n",
        "input_layer = tf.keras.layers.DenseFeatures(input_fc, name='inputs')(transformed)\n",
        "\n",
        "# Deep learning model\n",
        "d1 = tf.keras.layers.Dense(16, activation='relu', name='d1')(input_layer)\n",
        "d2 = tf.keras.layers.Dropout(0.25, name='d2')(d1)\n",
        "d3 = tf.keras.layers.Dense(16, activation='relu', name='d3')(d2)\n",
        "output = tf.keras.layers.Dense(1, activation='sigmoid', name='d4', bias_initializer=tf.keras.initializers.Constant())(d3)\n",
        "\n",
        "model = tf.keras.Model(inputs, output)\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=metrics)\n",
        "tf.keras.utils.plot_model(model, rankdir='LR')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer log_amount is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n",
            "WARNING:tensorflow:Layer inputs is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n"
          ]
        },
        {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7ki3oUvw0gw",
        "outputId": "3c139bb1-5f20-4887-e365-a4e67b04e92c"
      },
      "source": [
        "class_weight = {0: 0.5, 1: 289.4}\n",
        "# Trains the model for a fixed number of epochs\n",
        "history = TODO: _________(train_df, validation_data=eval_df, epochs=20, class_weight=class_weight)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:543: UserWarning: Input dict contained keys ['Time'] which did not match any model input. They will be ignored by the model.\n",
            "  [n for n in tensors.keys() if n not in ref_input_names])\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "111/111 [==============================] - 42s 376ms/step - loss: 4.5471 - accuracy: 0.9247 - precision: 0.0000e+00 - recall: 0.0000e+00 - roc_auc: 0.1159 - val_loss: 0.1194 - val_accuracy: 0.9987 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_roc_auc: 0.2051\n",
            "Epoch 2/20\n",
            "111/111 [==============================] - 39s 352ms/step - loss: 0.7188 - accuracy: 0.9794 - precision: 0.0366 - recall: 0.4048 - roc_auc: 0.7873 - val_loss: 0.2645 - val_accuracy: 0.9703 - val_precision: 0.0159 - val_recall: 0.3506 - val_roc_auc: 0.7818\n",
            "Epoch 3/20\n",
            "111/111 [==============================] - 40s 358ms/step - loss: 0.4399 - accuracy: 0.9464 - precision: 0.0245 - recall: 0.7277 - roc_auc: 0.8920 - val_loss: 0.3100 - val_accuracy: 0.9256 - val_precision: 0.0140 - val_recall: 0.7922 - val_roc_auc: 0.8860\n",
            "Epoch 4/20\n",
            "111/111 [==============================] - 38s 344ms/step - loss: 0.3751 - accuracy: 0.9278 - precision: 0.0204 - recall: 0.8193 - roc_auc: 0.9132 - val_loss: 0.3218 - val_accuracy: 0.9071 - val_precision: 0.0118 - val_recall: 0.8312 - val_roc_auc: 0.9160\n",
            "Epoch 5/20\n",
            "111/111 [==============================] - 37s 331ms/step - loss: 0.3428 - accuracy: 0.9203 - precision: 0.0192 - recall: 0.8482 - roc_auc: 0.9234 - val_loss: 0.3077 - val_accuracy: 0.9069 - val_precision: 0.0121 - val_recall: 0.8571 - val_roc_auc: 0.9343\n",
            "Epoch 6/20\n",
            "111/111 [==============================] - 40s 357ms/step - loss: 0.3096 - accuracy: 0.9186 - precision: 0.0188 - recall: 0.8482 - roc_auc: 0.9342 - val_loss: 0.2953 - val_accuracy: 0.9092 - val_precision: 0.0128 - val_recall: 0.8831 - val_roc_auc: 0.9461\n",
            "Epoch 7/20\n",
            "111/111 [==============================] - 38s 344ms/step - loss: 0.2940 - accuracy: 0.9218 - precision: 0.0194 - recall: 0.8410 - roc_auc: 0.9410 - val_loss: 0.2789 - val_accuracy: 0.9135 - val_precision: 0.0132 - val_recall: 0.8701 - val_roc_auc: 0.9538\n",
            "Epoch 8/20\n",
            "111/111 [==============================] - 38s 347ms/step - loss: 0.2791 - accuracy: 0.9251 - precision: 0.0206 - recall: 0.8602 - roc_auc: 0.9472 - val_loss: 0.2652 - val_accuracy: 0.9177 - val_precision: 0.0141 - val_recall: 0.8831 - val_roc_auc: 0.9601\n",
            "Epoch 9/20\n",
            "111/111 [==============================] - 40s 357ms/step - loss: 0.2666 - accuracy: 0.9252 - precision: 0.0208 - recall: 0.8651 - roc_auc: 0.9511 - val_loss: 0.2499 - val_accuracy: 0.9250 - val_precision: 0.0154 - val_recall: 0.8831 - val_roc_auc: 0.9657\n",
            "Epoch 10/20\n",
            "111/111 [==============================] - 40s 359ms/step - loss: 0.2697 - accuracy: 0.9281 - precision: 0.0215 - recall: 0.8602 - roc_auc: 0.9503 - val_loss: 0.2361 - val_accuracy: 0.9309 - val_precision: 0.0167 - val_recall: 0.8831 - val_roc_auc: 0.9692\n",
            "Epoch 11/20\n",
            "111/111 [==============================] - 38s 345ms/step - loss: 0.2420 - accuracy: 0.9313 - precision: 0.0234 - recall: 0.8964 - roc_auc: 0.9605 - val_loss: 0.2148 - val_accuracy: 0.9394 - val_precision: 0.0190 - val_recall: 0.8831 - val_roc_auc: 0.9715\n",
            "Epoch 12/20\n",
            "111/111 [==============================] - 39s 349ms/step - loss: 0.2438 - accuracy: 0.9356 - precision: 0.0243 - recall: 0.8747 - roc_auc: 0.9603 - val_loss: 0.2070 - val_accuracy: 0.9427 - val_precision: 0.0206 - val_recall: 0.9091 - val_roc_auc: 0.9749\n",
            "Epoch 13/20\n",
            "111/111 [==============================] - 37s 331ms/step - loss: 0.2274 - accuracy: 0.9349 - precision: 0.0245 - recall: 0.8916 - roc_auc: 0.9650 - val_loss: 0.2014 - val_accuracy: 0.9449 - val_precision: 0.0215 - val_recall: 0.9091 - val_roc_auc: 0.9771\n",
            "Epoch 14/20\n",
            "111/111 [==============================] - 39s 354ms/step - loss: 0.2371 - accuracy: 0.9404 - precision: 0.0267 - recall: 0.8916 - roc_auc: 0.9649 - val_loss: 0.1830 - val_accuracy: 0.9518 - val_precision: 0.0245 - val_recall: 0.9091 - val_roc_auc: 0.9782\n",
            "Epoch 15/20\n",
            "111/111 [==============================] - 40s 362ms/step - loss: 0.2077 - accuracy: 0.9422 - precision: 0.0276 - recall: 0.8916 - roc_auc: 0.9715 - val_loss: 0.1831 - val_accuracy: 0.9521 - val_precision: 0.0246 - val_recall: 0.9091 - val_roc_auc: 0.9798\n",
            "Epoch 16/20\n",
            "111/111 [==============================] - 40s 361ms/step - loss: 0.2099 - accuracy: 0.9465 - precision: 0.0295 - recall: 0.8843 - roc_auc: 0.9715 - val_loss: 0.1760 - val_accuracy: 0.9535 - val_precision: 0.0253 - val_recall: 0.9091 - val_roc_auc: 0.9806\n",
            "Epoch 17/20\n",
            "111/111 [==============================] - 40s 357ms/step - loss: 0.2113 - accuracy: 0.9457 - precision: 0.0295 - recall: 0.8988 - roc_auc: 0.9715 - val_loss: 0.1744 - val_accuracy: 0.9533 - val_precision: 0.0253 - val_recall: 0.9091 - val_roc_auc: 0.9821\n",
            "Epoch 18/20\n",
            "111/111 [==============================] - 40s 359ms/step - loss: 0.2023 - accuracy: 0.9471 - precision: 0.0304 - recall: 0.9036 - roc_auc: 0.9717 - val_loss: 0.1676 - val_accuracy: 0.9553 - val_precision: 0.0264 - val_recall: 0.9091 - val_roc_auc: 0.9820\n",
            "Epoch 19/20\n",
            "111/111 [==============================] - 41s 368ms/step - loss: 0.1963 - accuracy: 0.9489 - precision: 0.0315 - recall: 0.9060 - roc_auc: 0.9738 - val_loss: 0.1665 - val_accuracy: 0.9547 - val_precision: 0.0260 - val_recall: 0.9091 - val_roc_auc: 0.9821\n",
            "Epoch 20/20\n",
            "111/111 [==============================] - 40s 362ms/step - loss: 0.1980 - accuracy: 0.9488 - precision: 0.0313 - recall: 0.9012 - roc_auc: 0.9730 - val_loss: 0.1588 - val_accuracy: 0.9567 - val_precision: 0.0268 - val_recall: 0.8961 - val_roc_auc: 0.9825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7UsybOXw0gx",
        "outputId": "83838b3a-30b1-4bd5-ccea-99600694e1d7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['val_roc_auc']);\n",
        "plt.xlabel('Epoch');\n",
        "plt.ylabel('AUC');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fy7So6wyw0gx"
      },
      "source": [
        "## Load TensorFlow model into BigQuery\n",
        "\n",
        "Now that we have trained a TensorFlow model off BigQuery data ...\n",
        "let's load the model into BigQuery and use it for batch prediction!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT1DUegBw0gx",
        "outputId": "a4f9f70f-0078-403b-f882-f752f7d0f1c9"
      },
      "source": [
        "BUCKET='<your-bucket>'  # CHANGE TO SOMETHING THAT YOU OWN\n",
        "model.save('gs://{}/bqexample/export'.format(BUCKET))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2021-09-10 17:02:43.483098: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: gs://qwiklabs-gcp-03-5b2f0816822f/bqexample/export/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yS4w4Y4Xw0gx",
        "outputId": "d5e9cfa0-4f46-42a1-e3b7-ae250afe1e70"
      },
      "source": [
        "%%bigquery\n",
        "CREATE OR REPLACE MODEL advdata.keras_fraud_detection \n",
        "OPTIONS(model_type='tensorflow', model_path='gs://qwiklabs-gcp-03-5b2f0816822f/bqexample/export/*')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.00s: 100%|██████████| 2/2 [00:00<00:00, 1045.31query/s]                       \n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15aJZ_3Vw0gy"
      },
      "source": [
        "Now predict with this model (the reason it's called 'd4' is because the output node of my Keras model was called 'd4').\n",
        "To get probabilities, etc. we'd have to add the corresponding outputs to the Keras model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCzQwpZWw0gy",
        "outputId": "c441d9cb-a80f-47cb-9751-157b373f260e"
      },
      "source": [
        "%%bigquery\n",
        "SELECT d4, Class\n",
        "FROM ML.PREDICT( MODEL advdata.keras_fraud_detection,\n",
        "  (SELECT * FROM `bigquery-public-data.ml_datasets.ulb_fraud_detection` WHERE Time = 85285.0)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 384.30query/s]                          \n",
            "Downloading: 100%|██████████| 5/5 [00:01<00:00,  2.69rows/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>d4</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.110859</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.999994</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.999994</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999991</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.999991</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         d4  Class\n",
              "0  0.110859      0\n",
              "1  0.999994      1\n",
              "2  0.999994      1\n",
              "3  0.999991      1\n",
              "4  0.999991      1"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7bMPTqLw0gy"
      },
      "source": [
        "Copyright 2021 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ]
    }
  ]
}
